Blood group genotyping: faster and more reliable identification of rare blood for transfusion

Logical Analysis of Multiple Single-Nucleotide-Polymorphisms with Programmable DNA Molecular Computation for Clinical Diagnostics

Analyzing complex single-nucleotide-polymorphism (SNP) combinations in the genome is important for research and clinical applications, given that different SNP combinations can generate different phenotypic consequences. Recent works have shown that DNA-based molecular computing is powerful for simultaneously sensing and analyzing complex molecular information. Here, we designed a switching circuit-based DNA computational scheme that can integrate the sensing of multiple SNPs and simultaneously perform logical analysis of the detected SNP information to directly report clinical outcomes. As a demonstration, we successfully achieved automatic and accurate identification of 21 different blood group genotypes from 83 clinical blood samples with 100 % accuracy compared to sequencing data in a more rapid manner (3 hours). Our method enables a new mode of automatic and logical sensing and analyzing subtle molecular information for clinical diagnosis, as well as guiding personalized medication.

The experience of extended blood group genotyping by next-generation sequencing (NGS): investigation of patients with sickle-cell disease

BACKGROUND

Patients suffering from haemoglobinopathies may be treated by red blood cell (RBC) transfusion on a regular basis and then exposed to multiple antigens with a recurrent, potential risk of alloimmunization routinely prevented by extended RBC antigen cross-matching. While time-consuming and labour-intensive serological analyses are the gold standard for RBC typing, genotyping by current high-throughput molecular tools, including next-generation sequencing (NGS), appears to offer a potent alternative.

STUDY DESIGN AND METHODS

The potential of extended blood group genotyping (EBGG) by NGS of 17 genes involved in 14 blood group systems was evaluated in a cohort of 48 patients with sickle-cell disease. Sample preparation and sequencing were simplified and automated for future routine implementation.

RESULTS

Sequencing data were obtained for all DNA samples with two different sequencing machines. Prediction of phenotypes could be made in 12 blood group systems and partially in two other blood group systems (Rh and MNS). Importantly, predicted phenotypes in the MNS (S/s), Duffy, Kidd and Kell systems matched well with serological data (98·9%), when available. Unreferenced alleles in the ACHE and ART4 genes, respectively, involved in the Yt and Dombrock blood groups, were identified, then contributing to extend the current knowledge of blood group molecular genetics.

CONCLUSIONS

Overall, we consider that our strategy for NGS-based EBGG, assisted by a simple method for genotyping exons 1 and 2 of the pairs of homologous genes (i.e. RHD/RHCE and GYPA/GYPB), as well as the future support of potent bioinformatics tools, may be implemented for routine diagnosis in specific populations.